Short-Term Prediction Method for Gas Concentration in Poultry Houses Under Different Feeding Patterns
Ammonia (NH<sub>3</sub>) and carbon dioxide (CO<sub>2</sub>) are the main gases that affect indoor air quality and the health of the chicken flock. Currently, the environmental control strategy for poultry houses mainly relies on real-time temperature, resulting in lag and si...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-10-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/14/11/1891 |
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| Summary: | Ammonia (NH<sub>3</sub>) and carbon dioxide (CO<sub>2</sub>) are the main gases that affect indoor air quality and the health of the chicken flock. Currently, the environmental control strategy for poultry houses mainly relies on real-time temperature, resulting in lag and singleness. Indoor air quality can be improved by predicting the change in CO<sub>2</sub> concentration and proposing an optimal control strategy. Combining the advantages of seasonal-trend decomposition using loess (STL), Granger causality (GC), long short-term memory (LSTM), and extreme gradient boosting (XGBoost), an ensemble method called the STL-GC-LSTM-XGBoost model is proposed. This model can set fast response prediction results at a lower cost and has strong generalization ability. The comparative analysis shows that the proposed STL-GC-LSTM-XGBoost model achieved high prediction accuracy, performance, and confidence in predicting CO<sub>2</sub> levels under different environmental regulation modes and data volumes. However, its prediction accuracy for NH<sub>3</sub> was slightly lower than that of the STL-GC-LSTM model. This may be due to the limited variability and regularity of the NH<sub>3</sub> dataset, which likely increased model complexity and decreased predictive ability with the introduction of XGBoost. Nevertheless, in general, the proposed integrated model still provides a feasible approach for gas concentration prediction and health-related risk control in poultry houses. |
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| ISSN: | 2077-0472 |